# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
from tests.mark_utils import arg_mark

import numpy as np
import pytest

import mindspore.context as context
from mindspore import Tensor
import mindspore.ops.operations._grad_ops as P

context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
np.random.seed(1)


@arg_mark(plat_marks=['platform_gpu'], level_mark='level1', card_mark='onecard', essential_mark='unessential')
def test_asinhgrad_fp32():
    """
    Feature: asinh grad kernel
    Description: test asinh grad float32
    Expectation: just test
    """
    y_np = np.random.rand(4, 2).astype(np.float32) * 10
    dout_np = np.random.rand(4, 2).astype(np.float32) * 10
    output_ms = P.AsinhGrad()(Tensor(y_np), Tensor(dout_np))
    output_np = dout_np / np.cosh(y_np)
    assert np.allclose(output_ms.asnumpy(), output_np, 1e-4, 1e-4)


@arg_mark(plat_marks=['platform_gpu'], level_mark='level1', card_mark='onecard', essential_mark='unessential')
def test_asinhgrad_fp16():
    """
    Feature: asinh grad kernel
    Description: test asinh grad float16
    Expectation: just test
    """
    y_np = np.random.rand(4, 2).astype(np.float16) * 10
    dout_np = np.random.rand(4, 2).astype(np.float16) * 10
    output_ms = P.AsinhGrad()(Tensor(y_np), Tensor(dout_np))
    output_np = dout_np.astype(np.float32) / np.cosh(y_np).astype(np.float32)
    assert np.allclose(output_ms.asnumpy(), output_np.astype(np.float16), 1e-3, 1e-3)


@arg_mark(plat_marks=['platform_gpu'], level_mark='level1', card_mark='onecard', essential_mark='unessential')
def test_asinhgrad_fp64():
    """
    Feature: asinh grad kernel
    Description: test asinh grad float64
    Expectation: just test
    """
    y_np = np.random.rand(4, 2).astype(np.float64) * 10
    dout_np = np.ones((4, 2)).astype(np.float64) * 10
    output_ms = P.AsinhGrad()(Tensor(y_np), Tensor(dout_np))
    output_np = dout_np / np.cosh(y_np)
    assert np.allclose(output_ms.asnumpy(), output_np, 1e-5, 1e-5)


@arg_mark(plat_marks=['platform_gpu'], level_mark='level1', card_mark='onecard', essential_mark='unessential')
def test_asinhgrad_complex64():
    """
    Feature: asinh grad kernel
    Description: test asinh grad complex64
    Expectation: just test
    """
    y_np = np.random.rand(4, 2).astype(np.complex64) * 10
    dout_np = np.random.rand(4, 2).astype(np.complex64) * 10
    output_ms = P.AsinhGrad()(Tensor(y_np), Tensor(dout_np))
    output_np = dout_np / np.cosh(y_np)
    assert np.allclose(output_ms.asnumpy(), output_np, 1e-3, 1e-3)


@arg_mark(plat_marks=['platform_gpu'], level_mark='level1', card_mark='onecard', essential_mark='unessential')
def test_asinhgrad_complex128():
    """
    Feature: asinh grad kernel
    Description: test asinh grad complex128
    Expectation: just test
    """
    y_np = np.random.rand(4, 2).astype(np.complex128) * 10
    dout_np = np.random.rand(4, 2).astype(np.complex128) * 10
    output_ms = P.AsinhGrad()(Tensor(y_np), Tensor(dout_np))
    output_np = dout_np / np.cosh(y_np)
    assert np.allclose(output_ms.asnumpy(), output_np, 1e-6, 1e-6)
